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XPLANY is the new technology solution by Xenialab! The software is in Beta version and the documentation is under implementation.
Estimate shifts
The platform also implements a system for the prediction of shifts. This system is based on artificial intelligence models that implement recursive neural network architectures (LSTM with time series) trained on real call data to optimize the amount of workforce available.
This system makes it possible to understand how many resources can be needed on a certain day at a certain time and to configure predictions you need to hook into the customer's database.
From Manage ➡️ Estimate shifts it is possible to create a prediction, entering certain parameters.
Connector: which connector do you want to connect to take all previous data to do prediction
Target service level: % of managed interactions
Target time: seconds within which to respond
Maximum occupancy: % of employees' work time to consider
Shrinkage: to define for example in a day % break time of employees (so unproductive time)
Interval: intervals duration expressed in minutes, minimum 15
Custom average handle time: to measure the average time of manage interactions, entered in seconds
Query to be launched for the algorithm, to tell which database data to retrieve
Configure the query
To configure the query, in order to integrate for example XCALLY to XPLANY for voice calls, it's possible to use a string like that:
SELECT SUBSTRING_INDEX(lastdata, ',', 1) AS service, calldate AS date, duration |
With this configuration, all incoming calls in the queue are filtered on all services.
If you would instead filter only certain services, you can use this type of string to create the query:
SELECT SUBSTRING_INDEX(lastdata, ',', 1) AS service, calldate AS date, duration |
So with this specific configuration with XCALLY integration, all incoming calls in the Support and Sales queues will be filtered.
Remember that you can use any MySQL database, connecting it with XPLANY.
In fact with all database types, to build the query you need to have 3 columns:
service to manage
date of interaction, e.g. when the call was made, when an e-mail/sms was received…
duration of interaction expressed in seconds.
System always needs these 3 columns to launch the prediction and the query can be launched on all the various channels.
For instance if you want to run a query on the mail service, you can create a query like that:
SELECT MailAccountId as service, createdAt as date, TIMESTAMPDIFF (SECOND, createdAt, updatedAt) as duration from mail_interactions |
In this example you have selected all mail interaction, inserting CreatedAt as date and entering in duration the difference between date of first read message and the arrival of last interaction (obviously in the calculation of the interaction manage you need to remove the non-operating hours).
These are only examples and queries can be customised as required, depending on database type.
If you want to explore how to integrate XPLANY with XCALLY database click here
Collected data are then loaded into database, which is not a relational one, so it allows fast processing (compared to e.g. a mysql database).
Prediction
Clicking on Next, you can select spot for the service for which the prediction is to be applied:
Going on Next, the system shows statistics about prediction
Volumes of calls received
Average Handle Time (Aht): metric used to measure the average time of manage interactions
Fte: number of full time employees considered to be present to manage volume of estimated calls
Clicking on next, you can insert minimum number of employees to consider, minimum and maximum shift duration (entered in minutes) and range date and hour over which you want to make a prediction:
Clicking on next, the system shows a prediction of work shifts:
When you click on Save, the system copies all shifts created in the Scheduling section, from where you can add other resources if necessary or you can solve the scheduling, clicking on Solve Roster to assign unassigned shifts to employees.
Estimate shifts can be a useful tool to compare the prediction with the scheduling designed by the planner. For example it is possible that the planner had planned 8 employees for a shift, while the prediction considers 5 sufficient to still achieve the same SLA (Service Level Agreement), thus reducing management costs.